Proposal-free approaches To shake off the rely on proposals and avoid the drawback caused by them, many bottom-up approaches like [1]}, [2]}, [3]}, [4]} are introduced. These works are in various frameworks. The recent affinity-based methods obtain instance segmentation via affinity derivation [5]} and graph partition[6]}. This formulation is more similar to the perception and reasoning procedure of human beings and can handle more challenging scenarios. GMIS [5]} utilizes both region proposals and pixel affinities to segment images and SSAP [8]} outputs the affinity pyramid and then performs cascaded graph partition. However, the affinity kernels of GMIS and SSAP are sparse in angle and distance, leading to missing components of some instances due to loss of affinity connection. To this end, we propose ASIS which includes principles of generating effective and efficient affinity kernel based on dataset property to solve Arbitrary Shape Instance Segmentation and achieve great improvement on iShape.
